Radiol Cardiothorac Imaging 2021 Apr 8;3(2):e200477. Epub 2021 Apr 8.
Department of Radiology (K.A.H., N.Y., T.R., S.K., A.H.) and Department of Medicine (D.J.C.), University of California San Diego, 9452 Medical Center Dr, La Jolla, CA 92037; Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.).
Purpose: To develop a deep learning-based algorithm to stage the severity of chronic obstructive pulmonary disease (COPD) through quantification of emphysema and air trapping on CT images and to assess the ability of the proposed stages to prognosticate 5-year progression and mortality.
Materials And Methods: In this retrospective study, an algorithm using co-registration and lung segmentation was developed in-house to automate quantification of emphysema and air trapping from inspiratory and expiratory CT images. The algorithm was then tested in a separate group of 8951 patients from the COPD Genetic Epidemiology study (date range, 2007-2017). Read More